A biological system such as a yeast or mammalian cell is an immensely complicated set of interacting pathways involving environmental inputs, protein interactions, gene regulations, and so forth. Improved knowledge concerning these pathways would be of immense importance in many scientific and technological fields.
For but one example, identification or improved knowledge of the biological pathway(s) of action of a drug or drug candidate would be of great commercial and human importance. Although the primary molecular target of and cellular pathways affected by a drug are often known or suspected because the drug was originally selected by a specific drug screen, it is important to verify its action on such a primary pathway and to quantify its action along other secondary pathways which may be harmful, or may be beneficial, often in unsuspected ways. In other cases, such as in drug discovery, the primary pathways of action of a candidate drug are unknown, and these must be determined.
Drug discovery, a critical step in the development of treatments for human diseases, is presently dominated by two approaches neither of which directly provide information on the pathways of action of a drug candidate. A first approach begins with a screen for compounds that have a desired effect on a cell (e.g., induction of apoptosis), or organism (e.g., inhibition of angiogenesis) as measured in a specific biological assay. Compounds with the desired activity may then be modified to increase potency, stability, or other properties, and the modified compounds retested in the assay. This approach returns little or no information on the mechanisms of action or cellular pathways affected by the candidates.
A second approach to drug discovery involves testing numerous compounds for a specific effect on a known molecular target, typically a cloned gene sequence or an isolated enzyme or protein. For example, high-throughput assays can be developed in which numerous compounds can be tested for the ability to change the level of transcription from a specific promoter or the binding of identified proteins. Although the use of high-throughput screens is a powerful methodology for identifying drug candidates, again it provides little or no information about the effects of a compound at the cellular or organismal level, in particular information concerning the actual cellular pathways affected.
In fact, analysis of the pathway of efficacy and toxicity of candidate drugs can consume a significant fraction of the drug development process (see, e.g., Oliff et al., 1997, "Molecular Targets for Drug Development," in DeVita et al. Cancer: Principles & Practice of Oncology 5th Ed. 1997 Lippincott-Raven Publishers, Philadelphia). Therefore, methods of improving this analysis are of considerable current value.
In the past, it has been possible to glean some information to some extent about the pathways and mechanisms occurring inside a biological system of interest (including pathways of drug action) by simply observing the system's response to known inputs. The response observed has typically been gene expressions (i.e., mRNA abundances) and/or protein abundances. The inputs are experimental perturbations including genetic mutations (such as genetic deletions), drug treatments, and changes in environmental growth conditions.
However, it has been a usually hopeless task to try to infer the details of the system simply from the observed input-output relationships. Even if a pathway hypothesis is available, it has not been easy to determine if differential experiments provides adequate or efficient tests or confirmation of the pathway hypothesis. And even with such experiments, it has not always been known how to interpret their results in view of the pathway hypothesis.
Despite much effort and elaborate measurements, little concrete progress has been made in reconstructing the pathways of biological systems, much less their time-dependent interactions, from simple observations such as protein and mRNA concentrations (McAdams et al., 1995, Circuit simulation of genetic networks, Science 269:650-656; Reinitz et al., 1995, Mechanism of eve stripe formation, Mechanisms of Development 49:133-158).
One approach to this problem has been bringing modeling tools from other disciplines to bear on this problem. For example, boolean representations and network models familiar to the electrical engineering community have been applied to biological systems (Mikulecky, 1990, Modeling intestinal absorption and other nutrition-related processes using PSPICE and STELLA, J. of Ped. Gastroenterology and Nutrition 11:7-20; McAdams et al., 1995, Circuit simulation of genetic networks, Science 269:650-656). One application has been to the control of gene transcription during development, particularly during sequential organism development (Yuh et al., 1998, Genomic Cis-regulatory logic: Experimental and computational analysis of a sea urchin gene, Science 279:1896-1902).
The difficulties noted in developing and testing models of biological pathways in organisms has hindered effective use of the great advances recently made in biological measurement techniques.
These include recent advances in measuring gene transcription (Schena et al., 1995, Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science 270: 467-470; Lockhort et al., 1996, Expression monitoring by hybridization to high-density oligonucleotide arrays, Nature Biotechnology 14:1675-1680; Blanchard et al., 1996, Sequence to array: Probing the genome's secrets, Nature Biotechnology 14, 1649; U.S. Pat. No. 5,569,588, issued Oct. 29, 1996 to Ashby et al. for Methods for Drug Screening) and measuring protein abundances (McCormacket al., 1997, Direct analysis and identification of proteins in mixtures by LC,MS,MS and database searching at the low-femtomole level, Anal. Chem. 69(4):767-776; Chait, Trawling for proteins in the post-genome era, Nature Biotechnology 14:1544). Further technical advances have been made in the ability to modify and perturb biological systems, especially with individual genetic mutations throughout the genome (Mortensen et al., 1992, Production of homozygous mutant ES cells with a single targeting construct, Mol. Cell. Biol. 12:2391-2395; Wach et al., 1994, New heterologous modules for classical or PCR-based gene disruptions in Saccharomyces cerevisiae,'Yeast 10:1793-1808). And of course, if quantitative methods were available, the rapid increase in computing power available per unit cost would make their exploitation ever more cost effective.